Widespread Data Issues in Manufacturing
The Data Dilemma: Unpacking the Challenges in Manufacturing
Manufacturers stand at the crossroads of innovation and inertia. At the heart of this conundrum lies a familiar yet vexing issue: data. Both the Hexagon Advanced Manufacturing Report and the ESG State of DataOps reveal a unified story—data is simultaneously the industry's greatest asset and most insidious challenge. To thrive, manufacturers must address the widespread issues surrounding data quality, accessibility, and integration.
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The Scope of the Problem
The statistics are startling: 98% of manufacturers report data-related challenges. This near-universal figure isn’t merely a reflection of operational inefficiencies—it signals a structural issue at the heart of modern manufacturing. Data inaccuracies, incompleteness, and obsolescence not only hinder decision-making but also jeopardize the effectiveness of advanced technologies such as automation and AI.
Similarly, ESG’s findings highlight the pervasiveness of data-related struggles across industries. With 33% of organizations identifying data quality management as their most resource-intensive activity and 42% citing difficulties in integrating disparate sources, the challenge spans far beyond the manufacturing floor. It speaks to a broader failure to harmonize systems and processes in an increasingly digital and interconnected world.
A System Out of Sync
The Hexagon report paints a picture of an industry caught between aspiration and reality. On the one hand, manufacturers are eager to harness technologies like automated quality control and predictive analytics. On the other, they are encumbered by silos, outdated data, and integration challenges. This dissonance illustrates a system out of sync—a dynamic where ambition continually outpaces infrastructure.
ESG’s emphasis on multi-cloud environments further illuminates the issue. Half of all organizations surveyed use three or more cloud providers. While this setup promises flexibility and scalability, it introduces new layers of complexity, making data consistency and synchronization Herculean tasks. For manufacturers, this fractured landscape raises the stakes: poor data management isn’t just a technical hiccup—it can ripple across supply chains, delaying products, and undermining innovation.
The Implications for Technology
Advanced technologies depend on reliable data. Hexagon’s findings reveal that 52% of manufacturers anticipate significant improvements through automated design optimization and generative AI. However, these tools require seamless access to high-quality data. ESG underscores this dependency, highlighting that 54% of organizations cite improved data quality and accuracy as the primary benefit of DataOps strategies.
This dependency underscores a paradox: while manufacturers are eager to adopt cutting-edge technologies, they are often unprepared to meet the foundational requirements these systems demand. Inaccurate or incomplete data becomes a bottleneck, turning potentially transformative tools into expensive yet underperforming assets.
The Human Dimension
Amid the technological narrative, there’s a human story unfolding. Hexagon and ESG both highlight the role of collaboration—or lack thereof—in exacerbating data challenges. Hexagon notes that 42% of manufacturers face difficulties sharing data between teams, while ESG’s findings reveal that poor integration often stems from cultural and organizational misalignment.
These barriers extend beyond workflow inefficiencies. They represent a deeper cultural resistance to change, where teams struggle to adapt to a data-driven paradigm. The promise of innovation remains tethered to the willingness—and ability—of individuals to embrace new tools and approaches.
What It All Means
The widespread data issues plaguing manufacturing are more than operational headaches; they are a reflection of the growing pains of an industry undergoing rapid transformation. The Hexagon report likens this moment to a fork in the road, where manufacturers must navigate between technological aspiration and the weight of systemic inefficiencies. ESG reinforces this view, showing how these challenges mirror those faced across industries, particularly in complex, data-intensive environments.
This isn’t merely a story about technology or infrastructure—it’s about alignment. The disconnect between ambition and execution speaks to the need for a reevaluation of how data is perceived and utilized. For manufacturers, the stakes couldn’t be higher: in an era where speed, agility, and precision define competitiveness, the ability to manage data effectively will increasingly determine who leads and who lags.
In the end, the lesson from both reports is clear. Data is no longer just a supporting asset—it is the backbone of modern manufacturing. The industry's ability to evolve will depend not on the adoption of advanced technologies alone but on its capacity to address the systemic data challenges that continue to hold it back.
Three Pieces of Advice
Adopt a Federated Data Architecture
Action: Transition from a centralized data model to a federated architecture that blends central oversight with decentralized control. This enables domain-specific teams to manage and optimize their data for local needs while maintaining consistency and interoperability across the enterprise. This approach breaks down silos and allows for faster, more informed decision-making.
How to Start: Begin by identifying and categorizing your data domains (e.g., supply chain, production, quality control) and assigning ownership to teams closest to the data’s origin and use. Utilize distributed data integration techniques to ensure these domains remain connected under a unified governance structure.
Best Practice: Treat each domain’s data as a product, complete with clear ownership, quality metrics, and documentation. Form cross-functional working groups to align decentralized operations with overarching business goals, ensuring federated systems remain cohesive and adaptable.
Prioritize Data Quality and Governance
Action: Build robust systems and processes to ensure data is accurate, consistent, and timely. Strong governance policies and tools are essential to maintain the integrity of data as it moves through various pipelines and is consumed for insights and decisions. By addressing data quality at its source, manufacturers can prevent downstream errors and inefficiencies.
How to Start: Conduct a baseline data quality assessment to identify critical gaps and inconsistencies. Implement automated data quality monitoring and error detection processes to maintain high standards. Develop clear governance policies that include roles like data stewards to oversee the application of these standards.
Best Practice: Leverage a Manufacturing Execution System (MES) tightly integrated with your data platform to provide real-time, high-quality operational data. Such integration ensures that data collected on the shop floor—such as production cycles, defect rates, and equipment performance—is contextualized within broader analytics frameworks. This combination allows manufacturers to tie operational insights directly to strategic outcomes, such as process optimization and cost reduction.
Empower Business Users with Self-Service Analytics Software
Action: Provide business users with software that enables them to access, analyze, and act on data without requiring extensive technical expertise or IT intervention. The software must facilitate intuitive exploration of data, allow for customizable visualizations, and support real-time and historical data analysis. Empowering users reduces bottlenecks, speeds up decision-making, and fosters a data-driven culture across the organization.
How to Start: Introduce software that simplifies data discovery, enabling users to easily locate relevant datasets and generate insights without complex queries or code. The platform should offer flexible dashboarding capabilities for visualizing key performance indicators and include built-in mechanisms for collaboration, such as sharing insights and reports across teams.
Best Practice: Pair the rollout of such software with comprehensive training programs that teach employees not just how to use the tools but how to interpret and apply the data effectively. Establish internal support channels to address user questions and encourage adoption, while embedding governance policies within the software to ensure consistency and data integrity.
References:
Hexagon - 2024 Advanced Manufacturing Report: https://hexagon.com/advanced-manufacturing-report
Enterprise Strategy Group - 2023 State of DataOps, Unleashing the Power of Data: https://boomi.com/content/ebook/esg-state-of-data-ops/
